## libraries

library(colorout)
library(here)
library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggridges)
library(data.table)
library(abind)
library(httpgd)
source(here("src", "stroop-rsa-pc.R"))

## settings
hgd()
## httpgd server running at:
##   http://127.0.0.1:34269/live?token=xK51i3X5
opts_chunk$set(echo = TRUE)
theme_set(theme_bw(base_size = 10))

## constants
#subjlist <- "ispc_retest"
subjlist <- params$subjlist
if (subjlist == "ispc_retest") {
    session <- "reactive"
} else if (subjlist == "all_retest") {
    session <- "proactive"
}
prewhs <- c("none", "obsall")
glms <- c("lsall_1rpm", "lssep_1rpm")
roiset <- "Schaefer2018Dev"
measures <- c("cveuc", "crcor")

dat <- enlist(combo_paste(measures, glms, sep = "__"))
for (measure in measures) {
    for (glmname in glms) {
        for (prewh in prewhs) {
            if (prewh == "obsall" && glmname == "lssep_1rpm") next
            file_name <- 
                construct_filename_weights(
                    measure = measure, subjlist = subjlist, glmname = glmname, roiset = roiset, prewh = prewh
                    )
            dat[[paste0(measure, "__", glmname, "__", prewh)]] <- fread(file_name)
        }
    }
}
dat <- rbindlist(dat, idcol = "id")
dat <- separate(dat, id, c("measure", "glmname", "prewh"), sep = "__")


## calculate

## for group stats:
dat_sum <- dat %>% 
    group_by(prewh, measure, glmname, term, roi, subject) %>%    ## average over waves
    summarize(b = mean(b), .groups = "drop_last") %>%
    summarize(
        t_stat = t.test(b)$statistic, 
        p = t.test(b, alternative = "greater")$p.value, 
        .groups = "keep"
        )

## for test-retest correlations:
dat_r <- dat %>% 
    pivot_wider(names_from = "wave", values_from = "b") %>%  ## spread over waves
    group_by(prewh, measure, glmname, term, roi) %>%    ## correlate over subjects
    summarize(r = cor(wave1, wave2))
## `summarise()` has grouped output by 'prewh', 'measure', 'glmname', 'term'. You can override using the `.groups` argument.

ispc_retest

cross-run correlation

for (mod in models$crcor) {
    rdm <- 
        read_model_rdm(
            model = mod, measure_type = "similarity", session = session, ttype_subset = "bias"
        )
    p <- rdm %>% melt_mat %>% plot_melted_mat + labs(title = mod) + theme(axis.text.x = element_text(angle = 90, hjust = 0))
    print(p)
}

group stats

non-prewhitened

dat_sum %>%
    filter(measure == "crcor", prewh == "none") %>%
    ggplot(aes(roi, t_stat, fill = glmname)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level t stats")

dat %>%
    filter(measure == "crcor", prewh == "none") %>%
    ggplot(aes(roi, b, color = glmname)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
    facet_grid(cols = vars(term), scales = "free") +
    coord_flip() +
    scale_color_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level b coefficients")

dat_sum %>%
    filter(measure == "crcor", prewh == "none") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "t_stat") %>%
    ggplot(aes(lsall_1rpm, lssep_1rpm)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "group-level t stats")

prewhitened: lsall_1rpm

dat_sum %>%
    filter(measure == "crcor", glmname == "lsall_1rpm") %>%
    ggplot(aes(roi, t_stat, fill = prewh)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level t stats")

dat %>%
    filter(measure == "crcor", glmname == "lsall_1rpm") %>%
    ggplot(aes(roi, b, color = prewh)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
    facet_grid(cols = vars(term), scales = "free") +
    coord_flip() +
    scale_color_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level b coefficients")

dat_sum %>%
    filter(measure == "crcor", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "t_stat") %>%
    ggplot(aes(none, obsall)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "group-level t stats")

test-retest correlations

non-prewhitened

dat_r %>%
    filter(measure == "crcor", prewh == "none") %>%
    ggplot(aes(roi, r, fill = glmname)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "test-retest correlations")

dat_r %>%
    filter(measure == "crcor", prewh == "none") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "r") %>%
    ggplot(aes(lsall_1rpm, lssep_1rpm)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "test-retest correlations")

prewhitened: lsall_1rpm

dat_r %>%
    filter(measure == "crcor", glmname == "lsall_1rpm") %>%
    ggplot(aes(roi, r, fill = prewh)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "test-retest correlations")

dat_r %>%
    filter(measure == "crcor", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "r") %>%
    ggplot(aes(none, obsall)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "test-retest correlations")

table

non-prewhitened

dat_sum %>%
    full_join(dat_r) %>%
    filter(measure == "crcor", term %in% c("target", "distractor", "incongruency"), prewh == "none") %>%
    pivot_wider(names_from = c("glmname"), values_from = c("t_stat", "p", "r")) %>%
    filter(p_lsall_1rpm < 0.01 | p_lssep_1rpm < 0.01) %>%
    arrange(-t_stat_lsall_1rpm) %>%
    kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
prewh measure term roi t_stat_lsall_1rpm t_stat_lssep_1rpm p_lsall_1rpm p_lssep_1rpm r_lsall_1rpm r_lssep_1rpm
none crcor incongruency core32 9.502736 6.3897260 0.0000000 0.0000007 0.2576648 0.2335183
none crcor incongruency LH_Cont_PFCl_7 7.261195 5.0845563 0.0000001 0.0000168 -0.1752540 -0.2376095
none crcor incongruency LH_Cont_PFCl_6 6.789697 5.6141116 0.0000003 0.0000044 -0.0427431 -0.0457720
none crcor incongruency LH_Cont_Par_6 5.612558 5.1019862 0.0000044 0.0000161 0.3983638 0.1252006
none crcor incongruency LH_Cont_Par_5 5.583309 3.7054664 0.0000048 0.0005525 0.2819821 0.4817055
none crcor target SomMot 5.575581 2.6862448 0.0000049 0.0064535 0.4665613 0.2126629
none crcor incongruency RH_Cont_PFCl_10 5.562787 3.4542538 0.0000050 0.0010314 0.1725368 0.1659211
none crcor incongruency LH_Cont_Par_4 5.519772 6.8954603 0.0000056 0.0000002 0.1610696 -0.0830173
none crcor target core32 5.195800 2.4410332 0.0000127 0.0111985 0.1307804 0.1663915
none crcor target Vis 5.071893 3.6927566 0.0000173 0.0005704 -0.3160528 -0.1184354
none crcor incongruency RH_Cont_Par_6 5.021115 6.0957297 0.0000197 0.0000013 0.0794200 0.0317344
none crcor incongruency RH_Cont_PFCmp_2 4.982349 5.4893067 0.0000217 0.0000061 0.0342359 -0.0949051
none crcor incongruency Vis 4.764114 3.3254024 0.0000378 0.0014153 0.1747896 0.3290602
none crcor incongruency RH_Cont_PFCl_9 4.640427 4.2256040 0.0000518 0.0001488 0.0042833 0.3167308
none crcor incongruency SomMot 4.515746 3.6970519 0.0000711 0.0005643 -0.3188843 0.1161220
none crcor target LH_Cont_PFCl_6 4.400302 2.4889632 0.0000954 0.0100713 0.1788363 0.1139701
none crcor incongruency RH_Cont_Par_3 4.326460 4.1453358 0.0001151 0.0001824 0.0398596 0.6905693
none crcor incongruency RH_SalVentAttn_TempOccPar_7 4.196165 3.5035289 0.0001603 0.0009132 0.1801064 -0.1694748
none crcor target LH_Cont_Par_4 4.084932 3.3442709 0.0002125 0.0013515 0.2206006 -0.0850635
none crcor incongruency RH_Cont_Par_5 3.949320 2.9221142 0.0002994 0.0037308 0.2304019 0.3547817
none crcor incongruency RH_Cont_Par_4 3.849029 2.8895323 0.0003855 0.0040279 0.0088307 -0.1676727
none crcor incongruency RH_Cont_PFCl_12 3.814896 2.3535042 0.0004200 0.0135625 0.5873312 -0.0526499
none crcor target LH_Cont_PFCmp_1 3.773086 1.2370507 0.0004665 0.1140182 0.1078751 0.6161276
none crcor incongruency RH_Cont_Par_1 3.762138 2.1929619 0.0004795 0.0191199 -0.1801987 0.2773889
none crcor incongruency LH_Cont_Par_1 3.565316 3.3266650 0.0007835 0.0014110 0.1388359 0.1113204
none crcor target LH_SalVentAttn_FrOperIns_3 3.553459 1.8122085 0.0008069 0.0412424 -0.0124574 0.5002385
none crcor incongruency LH_Default_PFC_17 3.480163 0.8837651 0.0009675 0.1927954 -0.1962736 -0.1283018
none crcor distractor Vis 3.351892 4.1662580 0.0013265 0.0001729 0.0964963 -0.0971207
none crcor incongruency RH_Cont_PFCl_5 3.241190 3.1190428 0.0017378 0.0023345 0.1558147 -0.0721184
none crcor incongruency LH_Cont_PFCmp_1 3.239709 1.7215857 0.0017441 0.0490058 0.2583110 0.3332248
none crcor incongruency LH_SalVentAttn_FrOperIns_3 3.220722 1.5523326 0.0018263 0.0668355 0.1487451 0.3341931
none crcor target LH_Cont_Par_6 3.091580 0.8694935 0.0024936 0.1965952 0.4963062 0.2283710
none crcor incongruency RH_Cont_Par_2 3.083485 0.8275706 0.0025425 0.2080335 -0.2204560 -0.0772815
none crcor target LH_Cont_PFCl_7 2.955672 3.0637331 0.0034467 0.0026655 0.0959507 0.1209055
none crcor incongruency LH_Cont_PFCl_8 2.942640 2.2276914 0.0035545 0.0177669 0.1811416 0.4152203
none crcor target LH_Cont_Par_5 2.882296 2.0974141 0.0040969 0.0233350 -0.0002479 0.2204501
none crcor incongruency RH_Cont_PFCl_14 2.849601 1.0562324 0.0044228 0.1506894 0.4310112 -0.0871671
none crcor incongruency RH_Default_Par_4 2.847260 2.7689065 0.0044471 0.0053356 0.3831627 0.5012649
none crcor target RH_Cont_PFCl_10 2.646594 2.2872187 0.0070647 0.0156485 0.2023661 0.3918089
none crcor distractor core32 2.552531 1.1980145 0.0087383 0.1213045 -0.0556093 -0.0120731
none crcor target LH_Cont_Par_1 2.235597 2.5768039 0.0174714 0.0082742 -0.1217202 -0.0115136
none crcor incongruency LH_Cont_PFCl_3 2.000202 2.4959441 0.0284583 0.0099162 -0.2143729 0.2050491
none crcor distractor LH_Cont_Par_6 1.593226 2.5834549 0.0620980 0.0081511 -0.1705864 0.0505177
none crcor target RH_Cont_Par_2 1.211452 3.0988010 0.1187580 0.0024508 0.1210856 -0.1537462
none crcor incongruency LH_Default_Par_7 1.012014 2.7615993 0.1608129 0.0054266 0.2511376 0.3153828

prewhitened

dat_sum %>%
    full_join(dat_r) %>%
    filter(measure == "crcor", term %in% c("target", "distractor", "incongruency"), prewh == "obsall") %>%
    filter(p < 0.01) %>%
    arrange(-t_stat) %>%
    kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
prewh measure glmname term roi t_stat p r
obsall crcor lsall_1rpm distractor Vis 10.737053 0.0000000 0.1775124
obsall crcor lsall_1rpm incongruency LH_Cont_Par_6 7.218336 0.0000001 -0.2886772
obsall crcor lsall_1rpm incongruency LH_Cont_Par_4 4.872713 0.0000287 0.0156696
obsall crcor lsall_1rpm incongruency LH_Cont_PFCl_7 4.543906 0.0000662 0.0302930
obsall crcor lsall_1rpm incongruency RH_Cont_Par_3 4.185860 0.0001646 0.1385422
obsall crcor lsall_1rpm incongruency RH_Cont_Par_4 4.134695 0.0001873 0.1811765
obsall crcor lsall_1rpm incongruency LH_Cont_Par_1 3.856961 0.0003779 -0.2743050
obsall crcor lsall_1rpm incongruency RH_Cont_Par_1 3.777628 0.0004612 -0.2375258
obsall crcor lsall_1rpm incongruency RH_Cont_PFCl_12 3.630649 0.0006660 -0.0278333
obsall crcor lsall_1rpm incongruency LH_Cont_Par_5 3.498515 0.0009246 -0.0215286
obsall crcor lsall_1rpm incongruency RH_Cont_PFCl_14 3.170702 0.0020613 0.3362581
obsall crcor lsall_1rpm incongruency RH_Cont_PFCl_10 3.115371 0.0023552 0.0138127
obsall crcor lsall_1rpm distractor LH_Cont_PFCl_7 2.975527 0.0032884 -0.0415091
obsall crcor lsall_1rpm incongruency RH_Cont_Par_6 2.904081 0.0038926 0.3947670
obsall crcor lsall_1rpm distractor core32 2.851914 0.0043990 -0.3750544
obsall crcor lsall_1rpm target LH_Cont_Par_1 2.830503 0.0046244 -0.4224343
obsall crcor lsall_1rpm incongruency RH_Cont_PFCl_5 2.826347 0.0046694 0.0851799
obsall crcor lsall_1rpm incongruency LH_Default_PFC_17 2.802768 0.0049328 0.3867066
obsall crcor lsall_1rpm incongruency LH_SalVentAttn_FrOperIns_3 2.651940 0.0069792 -0.3727224
obsall crcor lsall_1rpm incongruency RH_Cont_PFCmp_2 2.586070 0.0081031 -0.2186587

All significant ROIs at \(\alpha < 0.01\) uncorrected. Sorted by t_stat_lsall_1rpm. r_* columns show test–retest correlations.

cross-validated euclidean

for (mod in models$cveuc) {
    rdm <- 
        read_model_rdm(
            model = mod, measure_type = "cvdistance", session = session, ttype_subset = "bias"
        )
    p <- rdm %>% melt_mat %>% plot_melted_mat + labs(title = mod) + theme(axis.text.x = element_text(angle = 90, hjust = 0))
    print(p)
}

group stats

non-prewhitened

dat_sum %>%
    filter(measure == "cveuc", prewh == "none") %>%
    ggplot(aes(roi, t_stat, fill = glmname)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level t stats")

dat %>%
    filter(measure == "cveuc", prewh == "none") %>%
    ggplot(aes(roi, b, color = glmname)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
    facet_grid(cols = vars(term), scales = "free") +
    coord_flip() +
    scale_color_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level b coefficients")

dat_sum %>%
    filter(measure == "cveuc", prewh == "none") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "t_stat") %>%
    ggplot(aes(lsall_1rpm, lssep_1rpm)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "group-level t stats")

prewhitened: lsall_1rpm

dat_sum %>%
    filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
    ggplot(aes(roi, t_stat, fill = prewh)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level t stats")

dat %>%
    filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
    ggplot(aes(roi, b, color = prewh)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
    facet_grid(cols = vars(term), scales = "free") +
    coord_flip() +
    scale_color_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "group-level b coefficients")

dat_sum %>%
    filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "t_stat") %>%
    ggplot(aes(none, obsall)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "group-level t stats")

test-retest correlations

non-prewhitened

dat_r %>%
    filter(measure == "cveuc", prewh == "none") %>%
    ggplot(aes(roi, r, fill = glmname)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "test-retest correlations")

dat_r %>%
    filter(measure == "cveuc", prewh == "none") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "r") %>%
    ggplot(aes(lsall_1rpm, lssep_1rpm)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "test-retest correlations")

prewhitened: lsall_1rpm

dat_r %>%
    filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
    ggplot(aes(roi, r, fill = prewh)) +
    geom_hline(yintercept = 0) +
    stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
    facet_grid(cols = vars(term)) +
    coord_flip() +
    scale_fill_brewer(type = "qual", palette = 2) +
    theme(legend.position = "top") +
    labs(title = "test-retest correlations")

dat_r %>%
    filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "r") %>%
    ggplot(aes(none, obsall)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(cols = vars(term)) +
    labs(title = "test-retest correlations")

table

dat_sum %>%
    full_join(dat_r) %>%
    filter(measure == "cveuc", prewh == "none", term %in% c("target", "distractor", "incongruency")) %>%
    pivot_wider(names_from = c("glmname"), values_from = c("t_stat", "p", "r")) %>%
    filter(p_lsall_1rpm < 0.01 | p_lssep_1rpm < 0.01) %>%
    arrange(-t_stat_lsall_1rpm) %>%
    kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
prewh measure term roi t_stat_lsall_1rpm t_stat_lssep_1rpm p_lsall_1rpm p_lssep_1rpm r_lsall_1rpm r_lssep_1rpm
none cveuc distractor Vis 5.093578 2.2664155 0.0000164 0.0163612 -0.0799357 -0.1582689
none cveuc incongruency LH_Cont_Par_4 4.653103 2.1740314 0.0000502 0.0198960 0.1515895 -0.4668176
none cveuc incongruency LH_Cont_PFCl_6 3.488767 1.2798539 0.0009471 0.1064152 0.0011426 -0.0843858
none cveuc incongruency core32 3.308457 1.5180810 0.0014752 0.0710289 -0.2843932 -0.2115412
none cveuc incongruency LH_Cont_PFCmp_1 3.022194 0.9525380 0.0029433 0.1751599 0.0431638 -0.3474682
none cveuc distractor LH_Default_Par_5 3.007858 0.7995982 0.0030455 0.2158934 -0.0481733 0.1110040
none cveuc incongruency LH_Cont_PFCl_7 2.737046 0.4055303 0.0057429 0.3443402 0.4018895 0.0907988
none cveuc incongruency Vis 2.661077 0.6202342 0.0068354 0.2704744 0.0341726 -0.3911705
none cveuc target RH_Cont_PFCv_1 1.998960 2.5420584 0.0285297 0.0089460 0.1700447 -0.0757452
none cveuc distractor LH_Default_PFC_17 1.340398 2.5061823 0.0963339 0.0096927 0.3342570 0.1077188 
dat_sum %>%
    full_join(dat_r) %>%
    filter(measure == "cveuc", prewh == "obsall", term %in% c("target", "distractor", "incongruency")) %>%
    filter(p < 0.01) %>%
    arrange(-t_stat) %>%
    kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
prewh measure glmname term roi t_stat p r
obsall cveuc lsall_1rpm distractor Vis 15.117831 0.0000000 0.3535142
obsall cveuc lsall_1rpm target Vis 12.065838 0.0000000 0.0615094
obsall cveuc lsall_1rpm target SomMot 11.488031 0.0000000 0.0404976
obsall cveuc lsall_1rpm target LH_Cont_PFCl_6 4.371572 0.0001027 -0.0105903
obsall cveuc lsall_1rpm incongruency core32 4.314150 0.0001188 0.1063221
obsall cveuc lsall_1rpm incongruency RH_Cont_PFCmp_2 4.276529 0.0001307 -0.2490124
obsall cveuc lsall_1rpm incongruency LH_Cont_PFCmp_1 3.756490 0.0004863 0.1149934
obsall cveuc lsall_1rpm incongruency Vis 3.747302 0.0004976 0.0524688
obsall cveuc lsall_1rpm target core32 3.645856 0.0006412 0.0285742
obsall cveuc lsall_1rpm incongruency LH_Cont_PFCl_6 3.344462 0.0013509 0.0078182
obsall cveuc lsall_1rpm incongruency RH_Cont_PFCv_1 3.136660 0.0022376 0.2376867
obsall cveuc lsall_1rpm incongruency LH_Cont_Par_4 2.967417 0.0033522 -0.3660298
obsall cveuc lsall_1rpm incongruency RH_Cont_PFCl_14 2.579422 0.0082255 0.0407573
obsall cveuc lsall_1rpm target LH_Cont_Par_6 2.573120 0.0083431 -0.2351706
obsall cveuc lsall_1rpm incongruency RH_SalVentAttn_TempOccPar_7 2.560175 0.0085896 0.0567755

All significant ROIs at \(\alpha < 0.01\) uncorrected. Sorted by t_stat_lsall_1rpm. r_* columns show test–retest correlations.

comparison of measures: cross-validated euclidean and cross-run correlation, lsall_1rpm

non-prewhitened euclidean

dat_sum %>%
    filter(term %in% c("distractor", "incongruency", "target"), prewh == "none", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "t_stat") %>%
    ggplot(aes(crcor, cveuc)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(rows = vars(glmname), cols = vars(term)) +
    labs(title = "t statistics", x = "cross-run correlation", y = "cross-validated euclidean")

dat_r %>%
    filter(term %in% c("distractor", "incongruency", "target"), prewh == "none", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "r") %>%
    ggplot(aes(crcor, cveuc)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(rows = vars(glmname), cols = vars(term)) +
    labs(title = "test--retest correlations", x = "cross-run correlation", y = "cross-validated euclidean")

prewhitened euclidean

dat_sum %>%
    filter(term %in% c("distractor", "incongruency", "target"), prewh == "obsall", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "t_stat") %>%
    ggplot(aes(crcor, cveuc)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(rows = vars(glmname), cols = vars(term)) +
    labs(title = "t statistics", x = "cross-run correlation", y = "cross-validated euclidean")

dat_r %>%
    filter(term %in% c("distractor", "incongruency", "target"), prewh == "obsall", glmname == "lsall_1rpm") %>%
    pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "r") %>%
    ggplot(aes(crcor, cveuc)) +
    geom_hline(yintercept = 0) +
    geom_vline(xintercept = 0) +
    geom_abline() +
    geom_point(size = 0.5) +
    facet_grid(rows = vars(glmname), cols = vars(term)) +
    labs(title = "test--retest correlations", x = "cross-run correlation", y = "cross-validated euclidean")